This invention relates to signal pattern recognition systems and, more particularly, to a training circuit used to assure uniform training and increased accuracy in reference patterns in such a system.
Signal pattern recognition systems have been provided in the prior art for use in automatically interpreting data signal patterns, most frequently audible voice signals. Such systems are described, for example, in patents previously issued to the assignee of the present invention, including U.S. Pat. Nos. 3,812,291 and 3,528,559. In addition, a pending patent application, assigned to the assignee of the present invention, Ser. No. 953,901, filed Oct. 23, 1978, now abandoned, and entitled Signal Pattern Encoder and Classifier, discloses a data compression system which is useful in limiting the buffer size required in such apparatus. In addition, a companion case to this application describes an improved buffer for use in accepting new data signals on a continuous basis while at the same time permitting serial accessing of data from the input buffer for test purposes.
Although the present state of the art is advanced, certain inconsistencies in the prior art in the use of training patterns to test and retrain reference patterns in the system's memory have caused inaccuracies in the recognition process, particularly in those systems wherein different reference patterns within the system's memory may be trained on the basis of different numbers of training patterns.
In addition, the prior art has not recognized the fact that, as training proceeds, a higher acceptance threshold should be provided in order to permit a broad range of training patterns in the early training passes, while only permitting a relatively narrow range as the training progresses. Additionally, the prior art has not provided a simple means for adjusting acceptance thresholds for training patterns when the training is undertaken in a noisy environment or through poor quality transmission, such as telephone transmission.
Furthermore, prior art devices have failed to properly handle the situation where training of a reference pattern proceeds with poor input training data, a situation which would indicate a problem in the reference pattern being trained.